

import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						lowerCamelCase__      ( *__A    :Dict	,__A    :Union[str, Any] = None	,__A    :str=True	,__A    :List[str]=2		):


       """simple docstring"""




       from .. import __version__

       __snake_case       	=							take_from
       __snake_case       	=							()
       if not isinstance(args[0]	,UpperCamelCase_		):
              __snake_case       	=							(args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(UpperCamelCase_		).base_version		) >= version.parse(UpperCamelCase_		):
                     raise ValueError(
                         F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                         F' version {__version__} is >= {version_name}'		)

              __snake_case       	=							None
              if isinstance(UpperCamelCase_	,UpperCamelCase_		) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(UpperCamelCase_		),)
                     __snake_case       	=							F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(UpperCamelCase_	,UpperCamelCase_		):
                     values += (getattr(UpperCamelCase_	,UpperCamelCase_		),)
                     __snake_case       	=							F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     __snake_case       	=							F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     __snake_case       	=							warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message	,UpperCamelCase_	,stacklevel=UpperCamelCase_		)

       if isinstance(UpperCamelCase_	,UpperCamelCase_		) and len(UpperCamelCase_		) > 0:
              __snake_case       	=							inspect.getouterframes(inspect.currentframe()		)[1]
              __snake_case       	=							call_frame.filename
              __snake_case       	=							call_frame.lineno
              __snake_case       	=							call_frame.function
              __snake_case	,    __snake_case       	=							next(iter(deprecated_kwargs.items()		)		)
              raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'		)

       if len(UpperCamelCase_		) == 0:
              return
       elif len(UpperCamelCase_		) == 1:
              return values[0]
       return values


# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
